From f4f0aa91a9163bd12bdc35659888471173ce5ac8 Mon Sep 17 00:00:00 2001
From: regisss <15324346+regisss@users.noreply.github.com>
Date: Fri, 5 Jan 2024 10:07:16 +0100
Subject: [PATCH] Update CI diff file

---
 tests/example_diff/run_generation.txt | 31 +++++++++++++++------------
 1 file changed, 17 insertions(+), 14 deletions(-)

diff --git a/tests/example_diff/run_generation.txt b/tests/example_diff/run_generation.txt
index 094d6f4a44..a29c040b55 100644
--- a/tests/example_diff/run_generation.txt
+++ b/tests/example_diff/run_generation.txt
@@ -498,25 +498,28 @@
 <         help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit",
 ---
 >         help="Whether to enable Habana Flash Attention, provided that the model supports it.",
-335,336d233
+335,336c234,235
 <     parser.add_argument("--jit", action="store_true", help="Whether or not to use jit trace to accelerate inference")
 <     args = parser.parse_args()
-338,339c235
+---
+>     parser.add_argument("--temperature", default=1.0, type=float, help="Temperature value for text generation")
+>     parser.add_argument("--top_p", default=1.0, type=float, help="Top_p value for generating text via sampling")
+338,339c237
 <     # Initialize the distributed state.
 <     distributed_state = PartialState(cpu=args.use_cpu)
 ---
 >     args = parser.parse_args()
-341c237,238
+341c239,240
 <     logger.warning(f"device: {distributed_state.device}, 16-bits inference: {args.fp16}")
 ---
 >     if not args.use_hpu_graphs:
 >         args.limit_hpu_graphs = False
-343,344c240
+343,344c242
 <     if args.seed is not None:
 <         set_seed(args.seed)
 ---
 >     return args
-346,373d241
+346,373d243
 <     # Initialize the model and tokenizer
 <     try:
 <         args.model_type = args.model_type.lower()
@@ -545,7 +548,7 @@
 <     if requires_preprocessing:
 <         prepare_input = PREPROCESSING_FUNCTIONS.get(args.model_type)
 <         preprocessed_prompt_text = prepare_input(args, model, tokenizer, prompt_text)
-375,378c243,246
+375,378c245,248
 <         if model.__class__.__name__ in ["TransfoXLLMHeadModel"]:
 <             tokenizer_kwargs = {"add_space_before_punct_symbol": True}
 <         else:
@@ -555,7 +558,7 @@
 >     parser = argparse.ArgumentParser()
 >     args = setup_parser(parser)
 >     model, tokenizer, generation_config = initialize_model(args, logger)
-380,386c248
+380,386c250
 <         encoded_prompt = tokenizer.encode(
 <             preprocessed_prompt_text, add_special_tokens=False, return_tensors="pt", **tokenizer_kwargs
 <         )
@@ -565,7 +568,7 @@
 <     encoded_prompt = encoded_prompt.to(distributed_state.device)
 ---
 >     import habana_frameworks.torch.hpu as torch_hpu
-388,389c250,393
+388,389c252,395
 <     if encoded_prompt.size()[-1] == 0:
 <         input_ids = None
 ---
@@ -713,7 +716,7 @@
 >         print(f"Graph compilation duration          = {compilation_duration} seconds")
 >         print(separator)
 >         print()
-391c395,412
+391c397,414
 <         input_ids = encoded_prompt
 ---
 >         # Downloading and loading a dataset from the hub.
@@ -734,7 +737,7 @@
 >             .shuffle()
 >             .select(range(args.dataset_max_samples if args.dataset_max_samples > 0 else (raw_dataset[split]).num_rows))
 >         )
-393,399c414,421
+393,399c416,423
 <     if args.jit:
 <         jit_input_texts = ["enable jit"]
 <         jit_inputs = prepare_jit_inputs(jit_input_texts, model, tokenizer)
@@ -751,7 +754,7 @@
 >             logger.info(
 >                 f"No column name was given so automatically choosing '{column_name}' for prompts. If you would like to use another column of the dataset, you can set the argument `--column_name`."
 >             )
-401,439c423,443
+401,439c425,445
 <             sig = inspect.signature(model.__call__)
 <         jit_inputs = tuple(jit_inputs[key] for key in sig.parameters if jit_inputs.get(key, None) is not None)
 <         traced_model = torch.jit.trace(model, jit_inputs, strict=False)
@@ -813,7 +816,7 @@
 >             preprocess_function,
 >             batched=True,
 >             desc="Running tokenizer on dataset",
-440a445,522
+440a447,524
 >         # After tokenization, we can remove the column of interest
 >         raw_dataset = raw_dataset.remove_columns([column_name])
 >         raw_dataset.set_format(type="torch")
@@ -892,13 +895,13 @@
 >             )
 >             print(separator)
 >         t_end = time.time()
-442,443c524,525
+442,443c526,527
 <         generated_sequences.append(total_sequence)
 <         print(total_sequence)
 ---
 >         throughput = total_new_tokens_generated / duration
 >         # Print Stats
-445c527,539
+445c529,541
 <     return generated_sequences
 ---
 >         stats = f"Throughput (including tokenization) = {throughput} tokens/second"